Peng H, Chi Z, Siu W
Center for Multimedia Signal Processing, Department of Electronic & Information Engineering, The Hong Kong Polytechnic University, Kowloon.
Int J Neural Syst. 2000 Apr;10(2):79-93. doi: 10.1142/S0129065700000089.
Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases.
非线性盲信号分离是一个重要但相当困难的问题。针对此类问题的任何通用非线性独立分量分析算法都应明确它试图找到哪种解决方案。最近的几种用于分离后非线性盲混合信号的神经网络仅限于对角非线性，即不存在跨通道非线性。本文提出了一种新的半参数混合神经网络，用于分离包含跨通道干扰的后非线性混合盲信号。这种混合网络由两个级联模块组成，一个是用于逼近后非线性的神经非线性模块，另一个是用于分离预测的线性盲混合信号的线性模块。非线性模块是一个由两个子网络组成的半参数展开式，其中一个是线性模型，另一个是三层感知器。这两个子网络共同产生一个“弱”非线性算子，并可通过调整参数来逼近相对较强的非线性。推导了一种基于熵最大化和梯度下降法的批学习算法。该模型成功应用于双源盲信号分离问题。我们的仿真结果表明，这种混合模型能够有效地逼近跨通道后非线性，并且在某些情况下能够实现良好的视觉质量以及高信噪比。
